In this notebook I'll be attempting to predict the stage 1 feed pressure. I'll compare two classical time series models with 2 machine learning models. The classical time series models are a simple autoregressive univariate model and an ARIMA model with exogenous variables. The machine learning models are a ridge regression and XGBoost model. The models are trained to make forecasts using the direct approach, in which the number of models trained equals the number of says into the future we want to forecast. Results show that the XGBoost model forecast produces the smallest RMSE even with only minimal hyperparameter tuning and feature engineering. Given the problem statement, RMSE is expected to be the most important metric as it focuses on the presence of large errors, which can be extremely costly for industrial equipment.
import scipy
import numpy as np
import pandas as pd
import sklearn
import math
import matplotlib.pyplot as plt
import plotly.express as px
import plotly.graph_objs as go
import plotly.io as pio
import statsmodels.api as sm
from plotly.subplots import make_subplots
from sklearn.linear_model import Ridge
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.model_selection import TimeSeriesSplit, GridSearchCV
from sklearn import metrics
from sklearn.svm import SVR
from sklearn.preprocessing import MinMaxScaler
from datetime import datetime
from pandas.plotting import autocorrelation_plot
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
import matplotlib.pyplot as plt
from statsmodels.tsa.stattools import adfuller
from sklearn.metrics import mean_squared_error, mean_absolute_error
import pmdarima as pm
from xgboost import XGBRegressor
svg_renderer = pio.renderers["svg"]
# define helper function for plotting
def make_plot(plot_type, df=None, x_vals=None, y_vals=None, plot_title=None, x_title=None, y_title=None, color_vals=None, error_vals=None):
tick_loc='inside'
tick_width=2
line_color='black'
line_width=2
xticks=7
x_tick_angle=45
x_tick_font_size=14
x_tick_font_color='black'
x_title_size=18
x_title_color="black"
yticks=7
y_tick_angle = 90
y_tick_font_size=14
y_tick_font_color = "black"
y_title_size=18
y_title_color="black"
plot_dict = {
'bar':px.bar(df, x=x_vals, y=y_vals, color=color_vals, error_y=error_vals),
'go': go.Figure(),
'box':px.box(df, y=y_vals, color=color_vals),
'histogram':px.histogram(df, x=x_vals),
'scatter': px.scatter(df, x=x_vals, y=y_vals, color=color_vals),
'line': px.line(df, x=x_vals, y=y_vals, color=color_vals)
}
fig = plot_dict[plot_type]
fig.update_yaxes(title=y_title, titlefont=dict(size=y_title_size, color=y_title_color),
ticks=tick_loc, nticks=yticks, tickwidth=tick_width,
tickfont=dict(size=y_tick_font_size, color=y_tick_font_color),
showline=True, linecolor=line_color, linewidth=line_width)
fig.update_xaxes(title=x_title, titlefont=dict(size=x_title_size, color=x_title_color),
ticks=tick_loc, nticks=xticks, tickwidth=tick_width,
showline=True, linecolor=line_color, linewidth=line_width,
tickangle=x_tick_angle, tickfont=dict(size=x_tick_font_size, color=x_tick_font_color))
# fig.update_traces(marker=dict(size=8,
# line = dict(width=2, color='DarkSlateGrey')))
fig.update_layout(title=plot_title,margin=dict(t=60), height=620, width=620, showlegend=True,
legend=dict(title='',
bordercolor="Black",
borderwidth=2,
yanchor="top",
y=1,
xanchor="left", font=dict(color='black'),
x=1))
return fig
raw_data = pd.read_csv("Technical Challenge Data For Release\\data_released.csv", index_col = 0, parse_dates = True)
raw_data.describe()
| feed_flow | product_flow | feed_concentration | product_concentration | feed_temperature | stage_1_feed_pressure | product_pressure | stage_2_feed_pressure | stage_3_feed_pressure | reject_pressure | ... | k2 | k3 | salt_loading | recovery | salt_loading_since_replace | salt_loading_since_clean | running_time_since_clean | running_time_since_replace | feed_ph | feed_turbidity | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 152075.000000 | 152075.000000 | 152075.000000 | 152075.000000 | 152075.000000 | 152075.000000 | 152075.000000 | 152075.000000 | 152075.000000 | 152075.000000 | ... | 152075.000000 | 152075.000000 | 152075.000000 | 152075.000000 | 152075.000000 | 162963.000000 | 450493.000000 | 450493.000000 | 323218.000000 | 57847.000000 |
| mean | 158.856637 | 127.173244 | 207.462287 | 12.347274 | 17.049586 | 12.514286 | 1.140823 | 9.599271 | 7.292033 | 5.133007 | ... | 0.010545 | 0.008176 | 0.032972 | 0.800573 | 411.792775 | 45.605274 | 396.366726 | 5472.112024 | 7.951497 | 0.055237 |
| std | 4.194192 | 3.374772 | 31.081676 | 1.987631 | 2.175214 | 1.388735 | 0.051323 | 0.734782 | 0.528891 | 0.423518 | ... | 0.001819 | 0.001374 | 0.005099 | 0.006082 | 67.755918 | 34.586714 | 274.994051 | 861.741686 | 0.228412 | 0.046973 |
| min | 119.578968 | 113.886360 | 62.302500 | 2.772000 | 10.868056 | 9.080715 | 0.863403 | 7.185452 | 5.497039 | 3.661245 | ... | 0.002000 | 0.000556 | 0.009196 | 0.771534 | 247.788085 | 0.000000 | 0.000000 | 3935.409545 | 5.001500 | 0.000000 |
| 25% | 157.011462 | 125.669268 | 208.080000 | 10.725754 | 16.090278 | 11.710300 | 1.107998 | 9.261709 | 7.058756 | 4.946301 | ... | 0.009390 | 0.007544 | 0.032528 | 0.796845 | 362.816867 | 14.296817 | 174.283333 | 4705.184545 | 7.882875 | 0.032188 |
| 50% | 159.608124 | 127.850868 | 218.430000 | 12.570754 | 16.394097 | 12.201560 | 1.133405 | 9.538371 | 7.268192 | 5.116952 | ... | 0.009862 | 0.007959 | 0.034691 | 0.800407 | 413.279150 | 41.039294 | 358.966667 | 5423.826212 | 7.911750 | 0.048125 |
| 75% | 161.961156 | 129.680208 | 224.910000 | 13.788000 | 16.687500 | 13.284940 | 1.187664 | 10.065840 | 7.653450 | 5.362586 | ... | 0.011214 | 0.008616 | 0.035960 | 0.804064 | 469.776495 | 78.371174 | 588.166667 | 6211.851212 | 7.970375 | 0.064687 |
| max | 169.294212 | 136.202292 | 258.885000 | 18.661500 | 25.663200 | 17.271980 | 1.886138 | 12.597170 | 10.071010 | 7.780146 | ... | 0.067401 | 0.225104 | 0.042418 | 1.000000 | 533.913118 | 108.666789 | 1067.925000 | 6892.226212 | 14.000000 | 3.239375 |
8 rows × 23 columns
Immediately I'll remove some of the columns that I won't be using to predict stage 1 feed pressure.
exclude_params = ['A', 'B', 'k1', 'k2', 'k3']
df = raw_data.drop(columns = exclude_params)
df.describe()
| feed_flow | product_flow | feed_concentration | product_concentration | feed_temperature | stage_1_feed_pressure | product_pressure | stage_2_feed_pressure | stage_3_feed_pressure | reject_pressure | salt_loading | recovery | salt_loading_since_replace | salt_loading_since_clean | running_time_since_clean | running_time_since_replace | feed_ph | feed_turbidity | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 152075.000000 | 152075.000000 | 152075.000000 | 152075.000000 | 152075.000000 | 152075.000000 | 152075.000000 | 152075.000000 | 152075.000000 | 152075.000000 | 152075.000000 | 152075.000000 | 152075.000000 | 162963.000000 | 450493.000000 | 450493.000000 | 323218.000000 | 57847.000000 |
| mean | 158.856637 | 127.173244 | 207.462287 | 12.347274 | 17.049586 | 12.514286 | 1.140823 | 9.599271 | 7.292033 | 5.133007 | 0.032972 | 0.800573 | 411.792775 | 45.605274 | 396.366726 | 5472.112024 | 7.951497 | 0.055237 |
| std | 4.194192 | 3.374772 | 31.081676 | 1.987631 | 2.175214 | 1.388735 | 0.051323 | 0.734782 | 0.528891 | 0.423518 | 0.005099 | 0.006082 | 67.755918 | 34.586714 | 274.994051 | 861.741686 | 0.228412 | 0.046973 |
| min | 119.578968 | 113.886360 | 62.302500 | 2.772000 | 10.868056 | 9.080715 | 0.863403 | 7.185452 | 5.497039 | 3.661245 | 0.009196 | 0.771534 | 247.788085 | 0.000000 | 0.000000 | 3935.409545 | 5.001500 | 0.000000 |
| 25% | 157.011462 | 125.669268 | 208.080000 | 10.725754 | 16.090278 | 11.710300 | 1.107998 | 9.261709 | 7.058756 | 4.946301 | 0.032528 | 0.796845 | 362.816867 | 14.296817 | 174.283333 | 4705.184545 | 7.882875 | 0.032188 |
| 50% | 159.608124 | 127.850868 | 218.430000 | 12.570754 | 16.394097 | 12.201560 | 1.133405 | 9.538371 | 7.268192 | 5.116952 | 0.034691 | 0.800407 | 413.279150 | 41.039294 | 358.966667 | 5423.826212 | 7.911750 | 0.048125 |
| 75% | 161.961156 | 129.680208 | 224.910000 | 13.788000 | 16.687500 | 13.284940 | 1.187664 | 10.065840 | 7.653450 | 5.362586 | 0.035960 | 0.804064 | 469.776495 | 78.371174 | 588.166667 | 6211.851212 | 7.970375 | 0.064687 |
| max | 169.294212 | 136.202292 | 258.885000 | 18.661500 | 25.663200 | 17.271980 | 1.886138 | 12.597170 | 10.071010 | 7.780146 | 0.042418 | 1.000000 | 533.913118 | 108.666789 | 1067.925000 | 6892.226212 | 14.000000 | 3.239375 |
# Number of NaNs
df.isnull().sum()
feed_flow 374964 product_flow 374964 feed_concentration 374964 product_concentration 374964 feed_temperature 374964 stage_1_feed_pressure 374964 product_pressure 374964 stage_2_feed_pressure 374964 stage_3_feed_pressure 374964 reject_pressure 374964 salt_loading 374964 recovery 374964 salt_loading_since_replace 374964 salt_loading_since_clean 364076 running_time_since_clean 76546 running_time_since_replace 76546 feed_ph 203821 feed_turbidity 469192 dtype: int64
df.shape
(527039, 18)
For now drop rows where the target variable is NaN. Independent variables will be interpolated.
# Expected remaining rows after removing NaN's
527039-374964
152075
df_nona = df.loc[~df['stage_1_feed_pressure'].isnull()]
df_nona.shape
(152075, 18)
fig = make_plot('line', df.reset_index(), 'Datetime', 'stage_1_feed_pressure', 'Stage 1 Feed Pressure', 'Date', 'Pressure (Bar)')
fig.update_layout(width=800, height=500)
fig.show(renderer="svg", scale=1)
fig2 = px.histogram(df.reset_index(), x='stage_1_feed_pressure', marginal='box')
fig2.show(renderer="svg", scale=1)
Right skew
corr = df.corr()
corr.style.background_gradient(cmap='coolwarm', axis=None)
| feed_flow | product_flow | feed_concentration | product_concentration | feed_temperature | stage_1_feed_pressure | product_pressure | stage_2_feed_pressure | stage_3_feed_pressure | reject_pressure | salt_loading | recovery | salt_loading_since_replace | salt_loading_since_clean | running_time_since_clean | running_time_since_replace | feed_ph | feed_turbidity | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| feed_flow | 1.000000 | 0.959897 | 0.120448 | 0.396639 | -0.143475 | -0.437724 | -0.001636 | -0.292629 | -0.200382 | -0.176142 | 0.293244 | -0.123099 | 0.408945 | -0.568855 | -0.496170 | 0.427239 | 0.038968 | -0.447636 |
| product_flow | 0.959897 | 1.000000 | 0.117762 | 0.392448 | -0.126821 | -0.456268 | 0.008686 | -0.318948 | -0.229149 | -0.200350 | 0.283935 | 0.159631 | 0.419447 | -0.578321 | -0.504917 | 0.437258 | 0.025401 | -0.463672 |
| feed_concentration | 0.120448 | 0.117762 | 1.000000 | 0.279031 | 0.131053 | -0.315488 | -0.119143 | -0.367262 | -0.350655 | -0.297440 | 0.984248 | -0.007894 | 0.241441 | -0.316449 | -0.192082 | 0.291055 | -0.225836 | -0.021863 |
| product_concentration | 0.396639 | 0.392448 | 0.279031 | 1.000000 | -0.180334 | -0.069812 | -0.031489 | -0.156170 | -0.234572 | -0.299987 | 0.338877 | -0.009754 | 0.665130 | -0.373002 | -0.227065 | 0.670405 | 0.032872 | -0.180923 |
| feed_temperature | -0.143475 | -0.126821 | 0.131053 | -0.180334 | 1.000000 | -0.595268 | -0.057836 | -0.761072 | -0.791180 | -0.754274 | 0.099934 | 0.057153 | -0.579823 | 0.005865 | -0.011367 | -0.522008 | 0.058914 | 0.102198 |
| stage_1_feed_pressure | -0.437724 | -0.456268 | -0.315488 | -0.069812 | -0.595268 | 1.000000 | 0.084853 | 0.932315 | 0.811257 | 0.677658 | -0.380413 | -0.074341 | 0.149585 | 0.594522 | 0.598244 | 0.089932 | -0.051657 | 0.245435 |
| product_pressure | -0.001636 | 0.008686 | -0.119143 | -0.031489 | -0.057836 | 0.084853 | 1.000000 | 0.145903 | 0.192154 | 0.229925 | -0.115439 | 0.037302 | -0.071989 | 0.023284 | -0.010587 | -0.075829 | 0.086991 | 0.012619 |
| stage_2_feed_pressure | -0.292629 | -0.318948 | -0.367262 | -0.156170 | -0.761072 | 0.932315 | 0.145903 | 1.000000 | 0.959234 | 0.872944 | -0.404189 | -0.098374 | 0.161596 | 0.480637 | 0.445109 | 0.095615 | -0.041610 | 0.125385 |
| stage_3_feed_pressure | -0.200382 | -0.229149 | -0.350655 | -0.234572 | -0.791180 | 0.811257 | 0.192154 | 0.959234 | 1.000000 | 0.970196 | -0.371387 | -0.104712 | 0.109839 | 0.323835 | 0.260953 | 0.050033 | -0.045420 | 0.077620 |
| reject_pressure | -0.176142 | -0.200350 | -0.297440 | -0.299987 | -0.754274 | 0.677658 | 0.229925 | 0.872944 | 0.970196 | 1.000000 | -0.315616 | -0.087784 | 0.066470 | 0.190398 | 0.116954 | 0.021160 | -0.058997 | 0.046762 |
| salt_loading | 0.293244 | 0.283935 | 0.984248 | 0.338877 | 0.099934 | -0.380413 | -0.115439 | -0.404189 | -0.371387 | -0.315616 | 1.000000 | -0.028042 | 0.307001 | -0.406730 | -0.274015 | 0.357976 | -0.212394 | -0.082329 |
| recovery | -0.123099 | 0.159631 | -0.007894 | -0.009754 | 0.057153 | -0.074341 | 0.037302 | -0.098374 | -0.104712 | -0.087784 | -0.028042 | 1.000000 | 0.041108 | -0.043647 | -0.039931 | 0.040077 | -0.046700 | 0.020546 |
| salt_loading_since_replace | 0.408945 | 0.419447 | 0.241441 | 0.665130 | -0.579823 | 0.149585 | -0.071989 | 0.161596 | 0.109839 | 0.066470 | 0.307001 | 0.041108 | 1.000000 | -0.365966 | -0.203387 | 0.992959 | -0.091175 | -0.277523 |
| salt_loading_since_clean | -0.568855 | -0.578321 | -0.316449 | -0.373002 | 0.005865 | 0.594522 | 0.023284 | 0.480637 | 0.323835 | 0.190398 | -0.406730 | -0.043647 | -0.365966 | 1.000000 | 0.964282 | -0.331768 | 0.072662 | 0.162722 |
| running_time_since_clean | -0.496170 | -0.504917 | -0.192082 | -0.227065 | -0.011367 | 0.598244 | -0.010587 | 0.445109 | 0.260953 | 0.116954 | -0.274015 | -0.039931 | -0.203387 | 0.964282 | 1.000000 | -0.078607 | 0.039066 | 0.212241 |
| running_time_since_replace | 0.427239 | 0.437258 | 0.291055 | 0.670405 | -0.522008 | 0.089932 | -0.075829 | 0.095615 | 0.050033 | 0.021160 | 0.357976 | 0.040077 | 0.992959 | -0.331768 | -0.078607 | 1.000000 | -0.075847 | -0.317879 |
| feed_ph | 0.038968 | 0.025401 | -0.225836 | 0.032872 | 0.058914 | -0.051657 | 0.086991 | -0.041610 | -0.045420 | -0.058997 | -0.212394 | -0.046700 | -0.091175 | 0.072662 | 0.039066 | -0.075847 | 1.000000 | -0.001527 |
| feed_turbidity | -0.447636 | -0.463672 | -0.021863 | -0.180923 | 0.102198 | 0.245435 | 0.012619 | 0.125385 | 0.077620 | 0.046762 | -0.082329 | 0.020546 | -0.277523 | 0.162722 | 0.212241 | -0.317879 | -0.001527 | 1.000000 |
corr['stage_1_feed_pressure'].sort_values()
feed_temperature -0.595268 product_flow -0.456268 feed_flow -0.437724 salt_loading -0.380413 feed_concentration -0.315488 recovery -0.074341 product_concentration -0.069812 feed_ph -0.051657 product_pressure 0.084853 running_time_since_replace 0.089932 salt_loading_since_replace 0.149585 feed_turbidity 0.245435 salt_loading_since_clean 0.594522 running_time_since_clean 0.598244 reject_pressure 0.677658 stage_3_feed_pressure 0.811257 stage_2_feed_pressure 0.932315 stage_1_feed_pressure 1.000000 Name: stage_1_feed_pressure, dtype: float64
Stage 1 feed pressure (S1FP) is most strongly correlated with reject pressure, running time and salt loading since clean/replace. Feed temperature, product flow, and feed flow are all negatively correlated with S1FP.
Will get a closer look at some of these using a scatterplot matrix.
fig3 = px.scatter_matrix(df, dimensions = ['stage_1_feed_pressure', 'feed_temperature', 'product_flow', 'reject_pressure', 'running_time_since_clean', 'salt_loading_since_clean'])
fig3.update_layout(width=1200, height = 1200)
fig4 = px.scatter(df, y='stage_1_feed_pressure', x='feed_temperature', color='running_time_since_clean')
fig4.show(renderer="svg", scale=1)
Feed temperature correlates with feed pressure most of the time. But sometimes feed pressure changes a lot even without changes in feed temperature. Something else must be happening. The color scale also shows that there are times where cleaning occurred yet feed pressure was still very high for a few minutes, this could be due to effect related to restarting the system or flushing out. In general though, increased running time led to higher feed pressure.
fig5 = px.scatter(df, y='stage_1_feed_pressure', x='running_time_since_clean', color='product_flow')
fig5.show(renderer="svg", scale=1)
When running time since clean becomes too high, essentially the membrane gets clogged and pressure increases dramatically. Not sure what causes those two satellite features at high running time since clean where feed pressure remains low.
fig6 = px.scatter(df, y='stage_1_feed_pressure', x='salt_loading_since_clean', color='feed_flow')
fig6.show(renderer="svg", scale=1)
Next I want to take a look at the timeseries for some of these features that are highly correlated with S1FP.
fig6 = make_plot('scatter', df=df.reset_index(), x_vals='Datetime', y_vals='running_time_since_clean', plot_title='Running Time Since Clean', x_title='Date', y_title="Time (H)")
fig6.show(renderer="svg", scale=1)
fig7 = make_plot('scatter', df=df.reset_index(), x_vals='Datetime', y_vals='running_time_since_replace', plot_title='Running Time Since Replace', x_title='Date', y_title="Time (H)")
fig7.show(renderer="svg", scale=1)
fig8 = make_plot('scatter', df=df.reset_index(), x_vals='Datetime', y_vals='salt_loading_since_clean', plot_title='Salt Loading Since Clean', x_title='Date', y_title="Salt Loading (kg)")
fig8.show(renderer="svg", scale=1)
fig9 = make_plot('scatter', df=df.reset_index(), x_vals='Datetime', y_vals='salt_loading_since_replace', plot_title='Salt Loading Since Replace', x_title='Date', y_title="Salt Loading (kg)")
fig9.show(renderer="svg", scale=1)
For ARIMA forecasts, will be able to simply use an increasing value for both of these parameters. Can also include forecasts that assume different max running times before cleaning. I won't have time to implement this now, though.
Will run some statistical analyses to determine the best type of classical model to use for this dataset. First I'm going to downsample the dataset to hourly just to make analysis quicker.
df_hourly = df_nona.resample('h').mean()
First step in deciding whether an autoregression model can be used is to see if the target variable exhibits autocorrelation.
df_hourly['stage_1_feed_pressure'].autocorr()
0.993133051802876
Next I'll plot the autocorrelation function plot to see at what lag values S1FP is autocorrelated.
def tsplot(y, lags=None, figsize=(12, 7), style='bmh'):
"""
Plot time series, its ACF and PACF, calculate Dickey–Fuller test
y - timeseries
lags - how many lags to include in ACF, PACF calculation
"""
if not isinstance(y, pd.Series):
y = pd.Series(y)
with plt.style.context(style):
fig = plt.figure(figsize=figsize)
layout = (2, 2)
ts_ax = plt.subplot2grid(layout, (0, 0), colspan=2)
acf_ax = plt.subplot2grid(layout, (1, 0))
pacf_ax = plt.subplot2grid(layout, (1, 1))
y.plot(ax=ts_ax)
p_value = sm.tsa.stattools.adfuller(y)[1]
ts_ax.set_title('Time Series Analysis Plots\n Dickey-Fuller: p={0:.5f}'.format(p_value))
plot_acf(y, lags=lags, ax=acf_ax)
plot_pacf(y, lags=lags, ax=pacf_ax)
plt.tight_layout()
# Identify a good maximum for lags
df['running_time_since_clean'].max()
1067.925
tsplot(df_hourly['stage_1_feed_pressure'].dropna(), lags=1000)
Strong, statistically significant autocorrelation is exhibited autocorrelation up until about 200 hours in advance. The presence of high autocorrelation for small lags indicates this is a trended time series and thus not stationary. There does not appear to be any seasonality with this dataset. Lack of stationarity means I'll need to difference the timeseries before using any classical methods such as ARIMA.
Next I'll plot the differenced values.
tsplot(df_hourly['stage_1_feed_pressure'].diff().dropna(), lags=1000)
Differencing the timeseries causes it to be slightly overdifferenced.
I'll use the augmented dickey-fuller test to further determine if the time series is stationary. Null hypothesis of this test is that there is the presence of a unit root, meaning we dont have stationarity. If we can reject this null H then we have stationarity.
def adfuller_test(series, signif=0.05, name='', verbose=False):
"""Perform ADFuller to test for Stationarity of given series and print report"""
r = adfuller(series, autolag='AIC')
output = {'test_statistic':round(r[0], 4), 'pvalue':round(r[1], 4), 'n_lags':round(r[2], 4), 'n_obs':r[3]}
p_value = output['pvalue']
def adjust(val, length= 6): return str(val).ljust(length)
# Print Summary
print(f' Augmented Dickey-Fuller Test on "{name}"', "\n ", '-'*47)
print(f' Null Hypothesis: Data has unit root. Non-Stationary.')
print(f' Significance Level = {signif}')
print(f' Test Statistic = {output["test_statistic"]}')
print(f' No. Lags Chosen = {output["n_lags"]}')
for key,val in r[4].items():
print(f' Critical value {adjust(key)} = {round(val, 3)}')
if p_value <= signif:
print(f" => P-Value = {p_value}. Rejecting Null Hypothesis.")
print(f" => Series is Stationary.")
else:
print(f" => P-Value = {p_value}. Weak evidence to reject the Null Hypothesis.")
print(f" => Series is Non-Stationary.")
for name, column in df_hourly.dropna().iteritems():
adfuller_test(column, name=column.name)
print('\n')
Augmented Dickey-Fuller Test on "feed_flow"
-----------------------------------------------
Null Hypothesis: Data has unit root. Non-Stationary.
Significance Level = 0.05
Test Statistic = -3.67
No. Lags Chosen = 2
Critical value 1% = -3.436
Critical value 5% = -2.864
Critical value 10% = -2.568
=> P-Value = 0.0046. Rejecting Null Hypothesis.
=> Series is Stationary.
Augmented Dickey-Fuller Test on "product_flow"
-----------------------------------------------
Null Hypothesis: Data has unit root. Non-Stationary.
Significance Level = 0.05
Test Statistic = -3.2614
No. Lags Chosen = 2
Critical value 1% = -3.436
Critical value 5% = -2.864
Critical value 10% = -2.568
=> P-Value = 0.0167. Rejecting Null Hypothesis.
=> Series is Stationary.
Augmented Dickey-Fuller Test on "feed_concentration"
-----------------------------------------------
Null Hypothesis: Data has unit root. Non-Stationary.
Significance Level = 0.05
Test Statistic = -3.755
No. Lags Chosen = 1
Critical value 1% = -3.436
Critical value 5% = -2.864
Critical value 10% = -2.568
=> P-Value = 0.0034. Rejecting Null Hypothesis.
=> Series is Stationary.
Augmented Dickey-Fuller Test on "product_concentration"
-----------------------------------------------
Null Hypothesis: Data has unit root. Non-Stationary.
Significance Level = 0.05
Test Statistic = -3.2113
No. Lags Chosen = 5
Critical value 1% = -3.436
Critical value 5% = -2.864
Critical value 10% = -2.568
=> P-Value = 0.0193. Rejecting Null Hypothesis.
=> Series is Stationary.
Augmented Dickey-Fuller Test on "feed_temperature"
-----------------------------------------------
Null Hypothesis: Data has unit root. Non-Stationary.
Significance Level = 0.05
Test Statistic = -2.413
No. Lags Chosen = 22
Critical value 1% = -3.436
Critical value 5% = -2.864
Critical value 10% = -2.568
=> P-Value = 0.1381. Weak evidence to reject the Null Hypothesis.
=> Series is Non-Stationary.
Augmented Dickey-Fuller Test on "stage_1_feed_pressure"
-----------------------------------------------
Null Hypothesis: Data has unit root. Non-Stationary.
Significance Level = 0.05
Test Statistic = -2.4426
No. Lags Chosen = 9
Critical value 1% = -3.436
Critical value 5% = -2.864
Critical value 10% = -2.568
=> P-Value = 0.1301. Weak evidence to reject the Null Hypothesis.
=> Series is Non-Stationary.
Augmented Dickey-Fuller Test on "product_pressure"
-----------------------------------------------
Null Hypothesis: Data has unit root. Non-Stationary.
Significance Level = 0.05
Test Statistic = -8.474
No. Lags Chosen = 6
Critical value 1% = -3.436
Critical value 5% = -2.864
Critical value 10% = -2.568
=> P-Value = 0.0. Rejecting Null Hypothesis.
=> Series is Stationary.
Augmented Dickey-Fuller Test on "stage_2_feed_pressure"
-----------------------------------------------
Null Hypothesis: Data has unit root. Non-Stationary.
Significance Level = 0.05
Test Statistic = -2.9205
No. Lags Chosen = 9
Critical value 1% = -3.436
Critical value 5% = -2.864
Critical value 10% = -2.568
=> P-Value = 0.043. Rejecting Null Hypothesis.
=> Series is Stationary.
Augmented Dickey-Fuller Test on "stage_3_feed_pressure"
-----------------------------------------------
Null Hypothesis: Data has unit root. Non-Stationary.
Significance Level = 0.05
Test Statistic = -3.3451
No. Lags Chosen = 9
Critical value 1% = -3.436
Critical value 5% = -2.864
Critical value 10% = -2.568
=> P-Value = 0.013. Rejecting Null Hypothesis.
=> Series is Stationary.
Augmented Dickey-Fuller Test on "reject_pressure"
-----------------------------------------------
Null Hypothesis: Data has unit root. Non-Stationary.
Significance Level = 0.05
Test Statistic = -3.6957
No. Lags Chosen = 9
Critical value 1% = -3.436
Critical value 5% = -2.864
Critical value 10% = -2.568
=> P-Value = 0.0042. Rejecting Null Hypothesis.
=> Series is Stationary.
Augmented Dickey-Fuller Test on "salt_loading"
-----------------------------------------------
Null Hypothesis: Data has unit root. Non-Stationary.
Significance Level = 0.05
Test Statistic = -3.2249
No. Lags Chosen = 3
Critical value 1% = -3.436
Critical value 5% = -2.864
Critical value 10% = -2.568
=> P-Value = 0.0186. Rejecting Null Hypothesis.
=> Series is Stationary.
Augmented Dickey-Fuller Test on "recovery"
-----------------------------------------------
Null Hypothesis: Data has unit root. Non-Stationary.
Significance Level = 0.05
Test Statistic = -4.3679
No. Lags Chosen = 15
Critical value 1% = -3.436
Critical value 5% = -2.864
Critical value 10% = -2.568
=> P-Value = 0.0003. Rejecting Null Hypothesis.
=> Series is Stationary.
Augmented Dickey-Fuller Test on "salt_loading_since_replace"
-----------------------------------------------
Null Hypothesis: Data has unit root. Non-Stationary.
Significance Level = 0.05
Test Statistic = -0.3492
No. Lags Chosen = 0
Critical value 1% = -3.436
Critical value 5% = -2.864
Critical value 10% = -2.568
=> P-Value = 0.9182. Weak evidence to reject the Null Hypothesis.
=> Series is Non-Stationary.
Augmented Dickey-Fuller Test on "salt_loading_since_clean"
-----------------------------------------------
Null Hypothesis: Data has unit root. Non-Stationary.
Significance Level = 0.05
Test Statistic = -2.5189
No. Lags Chosen = 0
Critical value 1% = -3.436
Critical value 5% = -2.864
Critical value 10% = -2.568
=> P-Value = 0.1109. Weak evidence to reject the Null Hypothesis.
=> Series is Non-Stationary.
Augmented Dickey-Fuller Test on "running_time_since_clean"
-----------------------------------------------
Null Hypothesis: Data has unit root. Non-Stationary.
Significance Level = 0.05
Test Statistic = -2.9701
No. Lags Chosen = 0
Critical value 1% = -3.436
Critical value 5% = -2.864
Critical value 10% = -2.568
=> P-Value = 0.0378. Rejecting Null Hypothesis.
=> Series is Stationary.
Augmented Dickey-Fuller Test on "running_time_since_replace"
-----------------------------------------------
Null Hypothesis: Data has unit root. Non-Stationary.
Significance Level = 0.05
Test Statistic = 0.1252
No. Lags Chosen = 0
Critical value 1% = -3.436
Critical value 5% = -2.864
Critical value 10% = -2.568
=> P-Value = 0.9677. Weak evidence to reject the Null Hypothesis.
=> Series is Non-Stationary.
Augmented Dickey-Fuller Test on "feed_ph"
-----------------------------------------------
Null Hypothesis: Data has unit root. Non-Stationary.
Significance Level = 0.05
Test Statistic = -6.1939
No. Lags Chosen = 6
Critical value 1% = -3.436
Critical value 5% = -2.864
Critical value 10% = -2.568
=> P-Value = 0.0. Rejecting Null Hypothesis.
=> Series is Stationary.
Augmented Dickey-Fuller Test on "feed_turbidity"
-----------------------------------------------
Null Hypothesis: Data has unit root. Non-Stationary.
Significance Level = 0.05
Test Statistic = -10.2847
No. Lags Chosen = 3
Critical value 1% = -3.436
Critical value 5% = -2.864
Critical value 10% = -2.568
=> P-Value = 0.0. Rejecting Null Hypothesis.
=> Series is Stationary.
As indicated with the ACF plot, the ADF test shows shows S1FP is non-stationary. I'll do the same test but for once-differenced data.
for name, column in df_hourly.diff().dropna().iteritems():
adfuller_test(column, name=column.name)
print('\n')
Augmented Dickey-Fuller Test on "feed_flow"
-----------------------------------------------
Null Hypothesis: Data has unit root. Non-Stationary.
Significance Level = 0.05
Test Statistic = -6.1223
No. Lags Chosen = 3
Critical value 1% = -3.45
Critical value 5% = -2.87
Critical value 10% = -2.571
=> P-Value = 0.0. Rejecting Null Hypothesis.
=> Series is Stationary.
Augmented Dickey-Fuller Test on "product_flow"
-----------------------------------------------
Null Hypothesis: Data has unit root. Non-Stationary.
Significance Level = 0.05
Test Statistic = -9.3206
No. Lags Chosen = 2
Critical value 1% = -3.45
Critical value 5% = -2.87
Critical value 10% = -2.571
=> P-Value = 0.0. Rejecting Null Hypothesis.
=> Series is Stationary.
Augmented Dickey-Fuller Test on "feed_concentration"
-----------------------------------------------
Null Hypothesis: Data has unit root. Non-Stationary.
Significance Level = 0.05
Test Statistic = -2.7631
No. Lags Chosen = 8
Critical value 1% = -3.45
Critical value 5% = -2.87
Critical value 10% = -2.571
=> P-Value = 0.0637. Weak evidence to reject the Null Hypothesis.
=> Series is Non-Stationary.
Augmented Dickey-Fuller Test on "product_concentration"
-----------------------------------------------
Null Hypothesis: Data has unit root. Non-Stationary.
Significance Level = 0.05
Test Statistic = -0.5523
No. Lags Chosen = 16
Critical value 1% = -3.451
Critical value 5% = -2.87
Critical value 10% = -2.572
=> P-Value = 0.8814. Weak evidence to reject the Null Hypothesis.
=> Series is Non-Stationary.
Augmented Dickey-Fuller Test on "feed_temperature"
-----------------------------------------------
Null Hypothesis: Data has unit root. Non-Stationary.
Significance Level = 0.05
Test Statistic = -14.8232
No. Lags Chosen = 0
Critical value 1% = -3.45
Critical value 5% = -2.87
Critical value 10% = -2.571
=> P-Value = 0.0. Rejecting Null Hypothesis.
=> Series is Stationary.
Augmented Dickey-Fuller Test on "stage_1_feed_pressure"
-----------------------------------------------
Null Hypothesis: Data has unit root. Non-Stationary.
Significance Level = 0.05
Test Statistic = -15.5351
No. Lags Chosen = 0
Critical value 1% = -3.45
Critical value 5% = -2.87
Critical value 10% = -2.571
=> P-Value = 0.0. Rejecting Null Hypothesis.
=> Series is Stationary.
Augmented Dickey-Fuller Test on "product_pressure"
-----------------------------------------------
Null Hypothesis: Data has unit root. Non-Stationary.
Significance Level = 0.05
Test Statistic = -17.9364
No. Lags Chosen = 0
Critical value 1% = -3.45
Critical value 5% = -2.87
Critical value 10% = -2.571
=> P-Value = 0.0. Rejecting Null Hypothesis.
=> Series is Stationary.
Augmented Dickey-Fuller Test on "stage_2_feed_pressure"
-----------------------------------------------
Null Hypothesis: Data has unit root. Non-Stationary.
Significance Level = 0.05
Test Statistic = -15.2176
No. Lags Chosen = 0
Critical value 1% = -3.45
Critical value 5% = -2.87
Critical value 10% = -2.571
=> P-Value = 0.0. Rejecting Null Hypothesis.
=> Series is Stationary.
Augmented Dickey-Fuller Test on "stage_3_feed_pressure"
-----------------------------------------------
Null Hypothesis: Data has unit root. Non-Stationary.
Significance Level = 0.05
Test Statistic = -14.8764
No. Lags Chosen = 0
Critical value 1% = -3.45
Critical value 5% = -2.87
Critical value 10% = -2.571
=> P-Value = 0.0. Rejecting Null Hypothesis.
=> Series is Stationary.
Augmented Dickey-Fuller Test on "reject_pressure"
-----------------------------------------------
Null Hypothesis: Data has unit root. Non-Stationary.
Significance Level = 0.05
Test Statistic = -14.6928
No. Lags Chosen = 0
Critical value 1% = -3.45
Critical value 5% = -2.87
Critical value 10% = -2.571
=> P-Value = 0.0. Rejecting Null Hypothesis.
=> Series is Stationary.
Augmented Dickey-Fuller Test on "salt_loading"
-----------------------------------------------
Null Hypothesis: Data has unit root. Non-Stationary.
Significance Level = 0.05
Test Statistic = -2.5373
No. Lags Chosen = 8
Critical value 1% = -3.45
Critical value 5% = -2.87
Critical value 10% = -2.571
=> P-Value = 0.1067. Weak evidence to reject the Null Hypothesis.
=> Series is Non-Stationary.
Augmented Dickey-Fuller Test on "recovery"
-----------------------------------------------
Null Hypothesis: Data has unit root. Non-Stationary.
Significance Level = 0.05
Test Statistic = -8.9883
No. Lags Chosen = 3
Critical value 1% = -3.45
Critical value 5% = -2.87
Critical value 10% = -2.571
=> P-Value = 0.0. Rejecting Null Hypothesis.
=> Series is Stationary.
Augmented Dickey-Fuller Test on "salt_loading_since_replace"
-----------------------------------------------
Null Hypothesis: Data has unit root. Non-Stationary.
Significance Level = 0.05
Test Statistic = -2.8302
No. Lags Chosen = 6
Critical value 1% = -3.45
Critical value 5% = -2.87
Critical value 10% = -2.571
=> P-Value = 0.0541. Weak evidence to reject the Null Hypothesis.
=> Series is Non-Stationary.
Augmented Dickey-Fuller Test on "salt_loading_since_clean"
-----------------------------------------------
Null Hypothesis: Data has unit root. Non-Stationary.
Significance Level = 0.05
Test Statistic = -2.9791
No. Lags Chosen = 6
Critical value 1% = -3.45
Critical value 5% = -2.87
Critical value 10% = -2.571
=> P-Value = 0.0369. Rejecting Null Hypothesis.
=> Series is Stationary.
Augmented Dickey-Fuller Test on "running_time_since_clean"
-----------------------------------------------
Null Hypothesis: Data has unit root. Non-Stationary.
Significance Level = 0.05
Test Statistic = -4.2686
No. Lags Chosen = 4
Critical value 1% = -3.45
Critical value 5% = -2.87
Critical value 10% = -2.571
=> P-Value = 0.0005. Rejecting Null Hypothesis.
=> Series is Stationary.
Augmented Dickey-Fuller Test on "running_time_since_replace"
-----------------------------------------------
Null Hypothesis: Data has unit root. Non-Stationary.
Significance Level = 0.05
Test Statistic = -4.1697
No. Lags Chosen = 4
Critical value 1% = -3.45
Critical value 5% = -2.87
Critical value 10% = -2.571
=> P-Value = 0.0007. Rejecting Null Hypothesis.
=> Series is Stationary.
Augmented Dickey-Fuller Test on "feed_ph"
-----------------------------------------------
Null Hypothesis: Data has unit root. Non-Stationary.
Significance Level = 0.05
Test Statistic = -5.7094
No. Lags Chosen = 4
Critical value 1% = -3.45
Critical value 5% = -2.87
Critical value 10% = -2.571
=> P-Value = 0.0. Rejecting Null Hypothesis.
=> Series is Stationary.
Augmented Dickey-Fuller Test on "feed_turbidity"
-----------------------------------------------
Null Hypothesis: Data has unit root. Non-Stationary.
Significance Level = 0.05
Test Statistic = -23.2601
No. Lags Chosen = 0
Critical value 1% = -3.45
Critical value 5% = -2.87
Critical value 10% = -2.571
=> P-Value = 0.0. Rejecting Null Hypothesis.
=> Series is Stationary.
Again, differencing the timeseries once causes it to become stationary.
I also want to get a closer look at the partial autocorrelation function to determine the order that is likely to be used in the ARIMA model.
plot_pacf(df_hourly['stage_1_feed_pressure'].dropna(), lags=20);
This indicates that when using an AR model, a lag order (p) of 3 or 4 may be most useful.
Check for missing data after downsampling to hourly.
df_hourly.isna().sum()
feed_flow 4691 product_flow 4691 feed_concentration 4691 product_concentration 4691 feed_temperature 4691 stage_1_feed_pressure 4691 product_pressure 4691 stage_2_feed_pressure 4691 stage_3_feed_pressure 4691 reject_pressure 4691 salt_loading 4691 recovery 4691 salt_loading_since_replace 4691 salt_loading_since_clean 4691 running_time_since_clean 4691 running_time_since_replace 4691 feed_ph 4691 feed_turbidity 7685 dtype: int64
For now I will simply drop those times. A more robust approach would be to interpolate them.
df_hourly_nona = df_hourly.loc[~df_hourly['stage_1_feed_pressure'].isnull()]
df_hourly_nona.isna().sum()
feed_flow 0 product_flow 0 feed_concentration 0 product_concentration 0 feed_temperature 0 stage_1_feed_pressure 0 product_pressure 0 stage_2_feed_pressure 0 stage_3_feed_pressure 0 reject_pressure 0 salt_loading 0 recovery 0 salt_loading_since_replace 0 salt_loading_since_clean 0 running_time_since_clean 0 running_time_since_replace 0 feed_ph 0 feed_turbidity 2994 dtype: int64
There's still some null values on feed_turbitidity. I'll fill those in using backward fill method.
df_hourly_nona=df_hourly_nona.fillna(method='bfill')
df_hourly_nona.isna().sum()
feed_flow 0 product_flow 0 feed_concentration 0 product_concentration 0 feed_temperature 0 stage_1_feed_pressure 0 product_pressure 0 stage_2_feed_pressure 0 stage_3_feed_pressure 0 reject_pressure 0 salt_loading 0 recovery 0 salt_loading_since_replace 0 salt_loading_since_clean 0 running_time_since_clean 0 running_time_since_replace 0 feed_ph 0 feed_turbidity 0 dtype: int64
Now I'll split the dataset into training and test. I'm choosing 70% since I want to capture the drastic increase is S1FP that occurred in April.
dataset = df_hourly_nona
split_index = int(.70 * dataset.shape[0])
train, test = dataset[:split_index], dataset[split_index:]
print('Observations: %d'% (len(dataset)))
print('Training Observations: %d'% (len(train)))
print('Testing Observations: %d'% (len(test)))
Observations: 4084 Training Observations: 2858 Testing Observations: 1226
First I'll make a simple univariate autoregression model. Below I show a plot of the training data in blue and test data in orange.
endog = train['stage_1_feed_pressure']
y_test = test['stage_1_feed_pressure']
endog.plot(figsize=(15, 5))
y_test.plot()
<AxesSubplot:xlabel='Datetime'>
First I'll construct the model using my own estimates that I arrived at from analysis in the previous section. Then I'll use auto_arima from the pmdarima package to automatically determine the best parameters through AIC minimzation.
Recall from analysis of the ACF and PACF plots that single difference (d=1) and lag order 3 (p=3) seemed appropriate.
# Construct the ARIMA model
model = sm.tsa.ARIMA(endog, order=(3, 1, 0))
# Estimate the parameters
model_fit = model.fit()
print(model_fit.summary())
ARIMA Model Results
===================================================================================
Dep. Variable: D.stage_1_feed_pressure No. Observations: 2857
Model: ARIMA(3, 1, 0) Log Likelihood 348.049
Method: css-mle S.D. of innovations 0.214
Date: Fri, 07 May 2021 AIC -686.097
Time: 12:52:10 BIC -656.310
Sample: 1 HQIC -675.356
=================================================================================================
coef std err z P>|z| [0.025 0.975]
-------------------------------------------------------------------------------------------------
const 3.334e-05 0.003 0.011 0.991 -0.006 0.006
ar.L1.D.stage_1_feed_pressure -0.2488 0.019 -13.301 0.000 -0.286 -0.212
ar.L2.D.stage_1_feed_pressure -0.0514 0.019 -2.669 0.008 -0.089 -0.014
ar.L3.D.stage_1_feed_pressure -0.0216 0.019 -1.157 0.247 -0.058 0.015
Roots
=============================================================================
Real Imaginary Modulus Frequency
-----------------------------------------------------------------------------
AR.1 -3.2353 -0.0000j 3.2353 -0.5000
AR.2 0.4303 -3.7550j 3.7796 -0.2318
AR.3 0.4303 +3.7550j 3.7796 0.2318
-----------------------------------------------------------------------------
C:\ProgramFilesForSoftwareHatingSpaces\Miniconda3\envs\py37\lib\site-packages\statsmodels\tsa\arima_model.py:472: FutureWarning:
statsmodels.tsa.arima_model.ARMA and statsmodels.tsa.arima_model.ARIMA have
been deprecated in favor of statsmodels.tsa.arima.model.ARIMA (note the .
between arima and model) and
statsmodels.tsa.SARIMAX. These will be removed after the 0.12 release.
statsmodels.tsa.arima.model.ARIMA makes use of the statespace framework and
is both well tested and maintained.
To silence this warning and continue using ARMA and ARIMA until they are
removed, use:
import warnings
warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARMA',
FutureWarning)
warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARIMA',
FutureWarning)
C:\ProgramFilesForSoftwareHatingSpaces\Miniconda3\envs\py37\lib\site-packages\statsmodels\tsa\base\tsa_model.py:583: ValueWarning:
A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
C:\ProgramFilesForSoftwareHatingSpaces\Miniconda3\envs\py37\lib\site-packages\statsmodels\tsa\base\tsa_model.py:583: ValueWarning:
A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
# Plot residual errors
residuals = pd.DataFrame(model_fit.resid)
fig, ax = plt.subplots(1,2)
residuals.plot(title="Residuals", ax=ax[0])
residuals.plot(kind='kde', title='Density', ax=ax[1])
plt.show()
<Figure size 2000x500 with 0 Axes>
The residuals of the trained model show that the for the training data, it fits well and follows the necessary assumptions for autoregression models. Namely that residuals are uncorrelated and have zero mean.
Next I'll make a forecast with 700 timesteps.
# Forecast
fc, se, conf = model_fit.forecast(700, alpha=0.05) # 95% conf
# Make as pandas series
fc_series = pd.Series(fc, index=test.iloc[:700].index)
lower_series = pd.Series(conf[:, 0], index=test.iloc[:700].index)
upper_series = pd.Series(conf[:, 1], index=test.iloc[:700].index)
# Plot
plt.figure(figsize=(12,5), dpi=100)
plt.plot(train['stage_1_feed_pressure'], label='training')
plt.plot(test['stage_1_feed_pressure'], label='actual')
plt.plot(fc_series, label='forecast')
plt.fill_between(lower_series.index, lower_series, upper_series,
color='k', alpha=.15)
plt.title('Forecast vs Actuals')
plt.legend(loc='upper left', fontsize=8)
plt.show()
# Accuracy metrics
def forecast_accuracy(forecast, actual):
mape = np.mean(np.abs(forecast - actual)/np.abs(actual)) # MAPE
me = np.mean(forecast - actual) # ME
mae = np.mean(np.abs(forecast - actual)) # MAE
mpe = np.mean((forecast - actual)/actual) # MPE
rmse = np.mean((forecast - actual)**2)**.5 # RMSE
R_sq = (np.corrcoef(forecast, actual)[0,1])**2 # R squared
return({'mape':mape, 'me':me, 'mae': mae,
'mpe': mpe, 'rmse':rmse,
'R_sq':R_sq})
test_values = test.iloc[:700]
forecast_accuracy(fc_series.values, test_values['stage_1_feed_pressure'])
{'mape': 0.04638435595304427,
'me': -0.22442402332545114,
'mae': 0.6010546697972393,
'mpe': -0.014761410212838904,
'rmse': 0.7367884588493598,
'R_sq': 0.262046971252497}
Not surprisingly, a univariate ARIMA model doesn't give very great results.
Now I'll run auto_arima to make sure I chose the best model.
auto_model = pm.auto_arima(endog, start_p=1, start_q=1,
test='adf', # use adftest to find optimal 'd'
max_p=3, max_q=3, # maximum p and q
m=1, # frequency of series
d=None, # let model determine 'd'
seasonal=False, # No Seasonality
start_P=0,
D=0,
trace=True,
error_action='ignore',
suppress_warnings=True,
stepwise=True)
print(auto_model.summary())
Performing stepwise search to minimize aic
ARIMA(1,1,1)(0,0,0)[0] intercept : AIC=-688.333, Time=0.66 sec
ARIMA(0,1,0)(0,0,0)[0] intercept : AIC=-519.716, Time=0.25 sec
ARIMA(1,1,0)(0,0,0)[0] intercept : AIC=-682.705, Time=0.21 sec
ARIMA(0,1,1)(0,0,0)[0] intercept : AIC=-690.330, Time=0.28 sec
ARIMA(0,1,0)(0,0,0)[0] : AIC=-521.716, Time=0.13 sec
ARIMA(0,1,2)(0,0,0)[0] intercept : AIC=-688.332, Time=0.72 sec
ARIMA(1,1,2)(0,0,0)[0] intercept : AIC=-686.342, Time=1.17 sec
ARIMA(0,1,1)(0,0,0)[0] : AIC=-692.330, Time=0.14 sec
ARIMA(1,1,1)(0,0,0)[0] : AIC=-690.333, Time=0.26 sec
ARIMA(0,1,2)(0,0,0)[0] : AIC=-690.332, Time=0.18 sec
ARIMA(1,1,0)(0,0,0)[0] : AIC=-684.705, Time=0.24 sec
ARIMA(1,1,2)(0,0,0)[0] : AIC=-688.479, Time=0.80 sec
Best model: ARIMA(0,1,1)(0,0,0)[0]
Total fit time: 5.077 seconds
SARIMAX Results
==============================================================================
Dep. Variable: y No. Observations: 2858
Model: SARIMAX(0, 1, 1) Log Likelihood 348.165
Date: Fri, 07 May 2021 AIC -692.330
Time: 13:51:13 BIC -680.415
Sample: 0 HQIC -688.034
- 2858
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ma.L1 -0.2494 0.011 -22.072 0.000 -0.272 -0.227
sigma2 0.0459 0.000 203.483 0.000 0.045 0.046
===================================================================================
Ljung-Box (L1) (Q): 0.00 Jarque-Bera (JB): 375414.47
Prob(Q): 1.00 Prob(JB): 0.00
Heteroskedasticity (H): 0.77 Skew: -2.07
Prob(H) (two-sided): 0.00 Kurtosis: 59.00
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
auto_model.plot_diagnostics(figsize=(10,8))
plt.show()
C:\ProgramFilesForSoftwareHatingSpaces\Miniconda3\envs\py37\lib\site-packages\statsmodels\graphics\gofplots.py:993: UserWarning: marker is redundantly defined by the 'marker' keyword argument and the fmt string "bo" (-> marker='o'). The keyword argument will take precedence.
Again, the fit model obeys rules for autoregressive models. Residual errors are around mean zero and have uniform variance. ACF plot of residual errors shows they're not correlated.
# Forecast
n_periods = 700
auto_fc, auto_confint = auto_model.predict(n_periods=n_periods, return_conf_int=True)
# make series for plotting purpose
auto_fc_series = pd.Series(auto_fc, index=test.iloc[:700].index)
auto_lower_series = pd.Series(auto_confint[:, 0], index=test.iloc[:700].index)
auto_upper_series = pd.Series(auto_confint[:, 1], index=test.iloc[:700].index)
# Plot
plt.figure(figsize=(12,5), dpi=100)
plt.plot(train['stage_1_feed_pressure'])
plt.plot(test['stage_1_feed_pressure'])
plt.plot(auto_fc_series, color='darkred')
plt.fill_between(auto_lower_series.index,
auto_lower_series,
auto_upper_series,
color='k', alpha=.15)
plt.plot(fc_series, color='darkgreen')
plt.fill_between(lower_series.index,
lower_series,
upper_series,
color='k', alpha=.15)
plt.title("Final Forecast of Usage")
plt.show()
Basically the same as my model.
forecast_accuracy(auto_fc_series, test_values['stage_1_feed_pressure'])
C:\ProgramFilesForSoftwareHatingSpaces\Miniconda3\envs\py37\lib\site-packages\numpy\lib\function_base.py:2642: RuntimeWarning: invalid value encountered in true_divide C:\ProgramFilesForSoftwareHatingSpaces\Miniconda3\envs\py37\lib\site-packages\numpy\lib\function_base.py:2643: RuntimeWarning: invalid value encountered in true_divide
{'mape': 0.04600222971501491,
'me': -0.23553152853950263,
'mae': 0.5968403260053154,
'mpe': -0.01565569197903466,
'rmse': 0.7369560719645348,
'R_sq': nan}
Here I will run a ARIMAX model fit with exogenous variables. For the time being I'll assume we know the values of the future exo vars when making the forecast. If I have time, I'll make a simple forecast for each of the exo vars which is then fed to this model when making final forecast.
UPDATE: I don't have time. But this would be quite easy to do in the future.
I'll choose relevant exogenous variables. These are also desired as they're easy to model themselves.
exo_vars = ['salt_loading_since_clean', 'running_time_since_clean', 'feed_temperature', 'product_flow']
model = pm.auto_arima(endog, exogenous=train.loc[:, exo_vars],
start_p=1, start_q=1,
test='adf',
max_p=12, max_q=12, m=1,
start_P=0, seasonal=False,
d=None, D=None, trace=True,
error_action='ignore',
suppress_warnings=True,
stepwise=True)
model.summary()
Performing stepwise search to minimize aic ARIMA(1,1,1)(0,0,0)[0] intercept : AIC=-3857.204, Time=2.83 sec ARIMA(0,1,0)(0,0,0)[0] intercept : AIC=-3475.288, Time=0.72 sec ARIMA(1,1,0)(0,0,0)[0] intercept : AIC=-3803.723, Time=2.61 sec ARIMA(0,1,1)(0,0,0)[0] intercept : AIC=-3858.668, Time=3.81 sec ARIMA(0,1,0)(0,0,0)[0] : AIC=-3477.281, Time=1.18 sec ARIMA(0,1,2)(0,0,0)[0] intercept : AIC=-3857.557, Time=3.23 sec ARIMA(1,1,2)(0,0,0)[0] intercept : AIC=-3857.707, Time=2.27 sec ARIMA(0,1,1)(0,0,0)[0] : AIC=-3860.341, Time=1.68 sec ARIMA(1,1,1)(0,0,0)[0] : AIC=-3861.160, Time=3.99 sec ARIMA(1,1,0)(0,0,0)[0] : AIC=-3805.726, Time=2.87 sec ARIMA(2,1,1)(0,0,0)[0] : AIC=-3848.982, Time=3.38 sec ARIMA(1,1,2)(0,0,0)[0] : AIC=-3859.908, Time=4.45 sec ARIMA(0,1,2)(0,0,0)[0] : AIC=-3859.819, Time=6.11 sec ARIMA(2,1,0)(0,0,0)[0] : AIC=-3841.170, Time=4.06 sec ARIMA(2,1,2)(0,0,0)[0] : AIC=-3858.965, Time=2.29 sec Best model: ARIMA(1,1,1)(0,0,0)[0] Total fit time: 45.510 seconds
| Dep. Variable: | y | No. Observations: | 2858 |
|---|---|---|---|
| Model: | SARIMAX(1, 1, 1) | Log Likelihood | 1937.580 |
| Date: | Fri, 07 May 2021 | AIC | -3861.160 |
| Time: | 13:14:55 | BIC | -3819.457 |
| Sample: | 0 | HQIC | -3846.123 |
| - 2858 | |||
| Covariance Type: | opg |
| coef | std err | z | P>|z| | [0.025 | 0.975] | |
|---|---|---|---|---|---|---|
| salt_loading_since_clean | -0.0039 | 0.003 | -1.286 | 0.199 | -0.010 | 0.002 |
| running_time_since_clean | 0.0005 | 0.000 | 1.716 | 0.086 | -7.51e-05 | 0.001 |
| feed_temperature | -0.3502 | 0.003 | -120.656 | 0.000 | -0.356 | -0.345 |
| product_flow | 0.0271 | 0.002 | 16.068 | 0.000 | 0.024 | 0.030 |
| ar.L1 | -0.0102 | 0.024 | -0.424 | 0.672 | -0.057 | 0.037 |
| ma.L1 | -0.3883 | 0.023 | -17.129 | 0.000 | -0.433 | -0.344 |
| sigma2 | 0.0151 | 4.15e-05 | 363.472 | 0.000 | 0.015 | 0.015 |
| Ljung-Box (L1) (Q): | 0.03 | Jarque-Bera (JB): | 9239138.70 |
|---|---|---|---|
| Prob(Q): | 0.86 | Prob(JB): | 0.00 |
| Heteroskedasticity (H): | 2.39 | Skew: | -10.21 |
| Prob(H) (two-sided): | 0.00 | Kurtosis: | 280.84 |
# Forecast
n_periods = test.shape[0]
fc, confint = model.predict(n_periods=n_periods, exogenous=test.loc[:,exo_vars], return_conf_int=True)
# make series for plotting purpose
fc_series = pd.Series(fc, index=test.index)
lower_series = pd.Series(confint[:, 0], index=test.index)
upper_series = pd.Series(confint[:, 1], index=test.index)
# Plot
plt.figure(figsize=(12,5), dpi=100)
plt.plot(train['stage_1_feed_pressure'])
plt.plot(test['stage_1_feed_pressure'])
plt.plot(fc_series, color='darkgreen')
plt.fill_between(lower_series.index,
lower_series,
upper_series,
color='k', alpha=.15)
plt.title("Stage 1 Feed Pressure Forecast")
plt.show()
C:\ProgramFilesForSoftwareHatingSpaces\Miniconda3\envs\py37\lib\site-packages\statsmodels\tsa\base\tsa_model.py:379: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
model.plot_diagnostics(figsize=(10,8))
plt.show()
C:\ProgramFilesForSoftwareHatingSpaces\Miniconda3\envs\py37\lib\site-packages\statsmodels\graphics\gofplots.py:993: UserWarning: marker is redundantly defined by the 'marker' keyword argument and the fmt string "bo" (-> marker='o'). The keyword argument will take precedence.
forecast_accuracy(fc_series, test['stage_1_feed_pressure'])
{'mape': 0.05035874164455471,
'me': 0.17504385836820882,
'mae': 0.6201234530245641,
'mpe': 0.017381573962736013,
'rmse': 0.7029694701658937,
'R_sq': 0.19222478453043285}
In this final section, I'll compare a ridge regression multivariable model with an XGBoost ensemble learner. For both models, I'll use direct method for multistep forecasting. This means I'll need to train as many models as time steps I want to forecast into the future.
I'm using SKLearn's GridSearchCV to aid in hyperparameter tuning, with a walk-forward cross validation approach. With more time, I'd increase the parameters across which GridSearchCV is run, leading to an overall better model. Also, with more time I'd do more feature engineering, such as rolling means of lag variables, categories of membrane age, and affect of pH on membrane degradation. Finally, given more time I'd like to use quartile regrssion to build prediciton intervals for the XGBoost model.
Below are the two functions I'll be using to train the models for each time-step and then save the forecast.
def direct_forecast(max_lag, training_fraction, forecast_steps, model_selection, dataset):
forecast_dates = []
predicted = []
for h in range(1, forecast_steps):
print(f'Forecasting for step {h}')
# Make a list of relevant columns.
X_cols = [col for col in dataset.columns if col not in ['stage_2_feed_pressure', 'stage_3_feed_pressure']]
# Make a dataframe consisting of only those columns
X = dataset.loc[:, X_cols]
# Create a lag for each variable up to 10 days. This will create NaNs at the beginning of the dataframe
for i in range(1, max_lag):
shifted = dataset.shift(i)
for col in X_cols:
X[col+'-'+str(i)] = shifted[col].values
#Remove the rows with NaNs caused by lag variable creation
X = X.iloc[(10-1):]
# Create a target variable, which is the value of stage feed pressure h days in advance
X['target+' + str(h)] = X['stage_1_feed_pressure'].shift(-h)
# Save that column as a new dataframe. Note that the date associated with this dataframe is h days behind the true value's location
y = X['target+' + str(h)]
# Drop the target variable and the stage 1 feed pressure columns from the X matrix
X = X.drop(columns=['stage_1_feed_pressure', 'target+' + str(h)])
# Determine the index location for 70% of our dataset. This should always be the same.
train_fraction_index = int(X.shape[0] *training_fraction)
# Create training featureset and target
X_train = X.iloc[:train_fraction_index]
y_train = y.iloc[:train_fraction_index]
# Create test dataframes. Shouldn't this only be plus one? Yes, since we're always testing on the day after our training set it complete. y_test will have the date of our first day after the training set, but will contain a value from h days in the future.
X_test = X.iloc[train_fraction_index:train_fraction_index+1]
y_test = y.iloc[train_fraction_index:train_fraction_index+1]
# Get the real date of our test value, which is h days ahead of our current position
corrected_date = y.index[train_fraction_index+h]
forecast_dates.append(corrected_date)
grid_search = fit_model(model_selection, X_train, y_train)
y_predict=grid_search.predict(X_test)
predicted.append(y_predict[-1])
return forecast_dates, predicted, grid_search
def fit_model(model_selection, X_train, y_train):
if model_selection == 'xgboost':
model = XGBRegressor()
pipeline = Pipeline([('scaler', MinMaxScaler()), ('model',model)])
tscv = TimeSeriesSplit(n_splits=3)
params = {'model__max_depth':[10],
'model__n_estimators': [10],
'model__subsample': [0.8],
'model__eta': [0.3],
'model__seed': [42]}
grid_search = GridSearchCV(pipeline, params, cv=tscv, verbose=2, scoring='neg_root_mean_squared_error', return_train_score=True)
fit = grid_search.fit(X_train.to_numpy(), y_train.to_numpy())
cv_results = pd.DataFrame(grid_search.cv_results_)
if model_selection == 'ridge':
regr = Ridge()
pipeline = Pipeline([('scaler', StandardScaler()), ('regr',regr)])
tscv = TimeSeriesSplit(n_splits=5)
params = {'regr__alpha':[0,1,2,10,100]}
grid_search = GridSearchCV(pipeline, params, cv=tscv, verbose=1, scoring='neg_root_mean_squared_error', return_train_score=True)
regr_fit = grid_search.fit(X_train.to_numpy(),y_train.to_numpy())
cv_results = pd.DataFrame(grid_search.cv_results_)
return grid_search
dataset = df_hourly_nona
forecast_dates, predicted, model = direct_forecast(max_lag=10, training_fraction=.70, forecast_steps=700, model_selection='xgboost', dataset=dataset)
xgboost_final_prediction = pd.Series(predicted, index=forecast_dates)
Forecasting for step 1 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 2 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 3 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.1s Forecasting for step 4 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 5 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 6 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 7 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 8 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 9 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 10 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 11 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 12 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 13 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 14 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 15 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 16 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 17 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 18 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 19 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 20 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 21 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 22 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 23 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 24 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 25 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 26 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 27 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 28 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 29 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 30 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 31 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 32 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 33 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 34 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 35 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 36 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 37 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 38 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 39 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 40 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 41 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 42 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 43 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 44 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 45 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 46 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 47 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 48 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 49 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 50 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 51 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 52 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 53 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 54 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 55 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 56 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 57 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 58 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 59 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 60 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 61 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 62 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 63 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 64 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 65 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 66 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 67 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 68 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 69 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 70 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 71 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 72 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 73 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 74 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 75 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 76 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 77 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 78 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 79 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 80 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 81 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 82 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 83 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 84 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 85 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 86 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 87 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 88 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 89 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 90 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 91 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 92 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 93 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 94 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 95 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 96 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 97 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 98 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 99 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 100 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 101 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 102 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 103 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 104 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 105 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 106 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 107 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 108 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 109 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 110 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 111 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 112 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 113 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 114 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.1s Forecasting for step 115 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 116 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 117 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 118 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 119 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 120 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 121 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 122 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 123 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 124 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 125 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 126 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 127 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 128 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 129 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 130 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 131 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 132 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 133 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 134 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 135 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 136 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 137 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 138 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 139 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 140 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 141 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 142 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 143 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 144 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 145 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 146 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 147 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 148 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 149 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 150 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 151 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 152 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 153 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 154 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 155 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 156 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 157 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 158 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 159 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 160 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 161 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 162 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 163 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 164 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 165 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 166 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 167 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 168 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 169 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 170 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 171 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 172 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 173 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 174 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 175 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 176 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 177 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 178 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 179 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 180 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 181 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 182 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 183 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 184 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 185 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 186 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 187 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 188 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 189 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 190 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 191 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 192 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 193 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 194 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 195 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 196 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 197 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 198 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 199 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 200 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 201 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 202 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 203 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.2s Forecasting for step 204 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 205 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 206 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 207 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 208 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 209 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 210 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 211 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 212 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 213 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 214 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 215 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 216 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 217 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 218 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 219 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 220 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 221 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 222 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 223 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 224 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 225 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 226 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 227 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 228 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 229 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 230 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 231 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 232 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 233 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 234 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 235 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 236 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 237 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 238 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 239 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 240 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 241 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 242 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 243 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 244 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 245 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 246 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 247 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 248 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 249 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 250 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 251 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 252 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 253 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 254 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 255 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 256 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 257 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 258 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 259 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 260 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 261 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 262 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 263 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 264 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 265 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 266 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 267 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 268 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 269 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 270 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 271 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 272 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 273 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 274 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 275 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 276 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 277 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 278 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 279 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 280 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 281 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 282 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 283 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 284 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 285 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 286 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 287 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 288 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 289 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 290 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 291 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 292 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 293 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 294 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 295 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 296 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 297 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 298 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 299 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 300 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 301 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 302 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 303 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 304 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 305 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 306 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 307 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 308 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 309 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 310 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 311 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 312 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 313 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 314 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 315 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 316 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 317 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 318 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 319 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 320 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 321 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 322 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 323 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 324 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.5s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 325 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 326 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 327 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 328 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 329 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 330 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 331 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 332 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 333 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 334 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 335 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 336 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.1s Forecasting for step 337 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 338 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 339 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 340 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 341 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 342 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 343 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 344 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 345 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 346 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 347 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 348 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 349 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 350 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 351 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 352 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 353 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 354 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 355 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 356 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 357 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 358 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 359 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 360 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 361 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 362 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 363 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 364 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 365 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 366 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 367 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 368 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 369 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 370 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 371 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 372 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 373 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 374 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 375 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 376 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 377 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 378 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 379 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 380 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 381 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 382 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 383 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 384 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 385 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 386 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 387 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 388 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 389 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 390 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 391 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 392 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 393 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 394 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 395 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 396 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 397 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 398 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 399 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 400 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 401 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 402 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 403 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 404 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 405 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 406 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 407 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 408 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 409 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 410 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 411 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 412 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 413 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 414 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 415 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 416 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 417 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.2s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.2s Forecasting for step 418 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 419 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 420 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 421 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 422 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 423 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 424 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 425 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 426 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 427 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 428 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 429 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 430 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 431 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 432 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 433 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 434 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 435 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 436 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 437 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 438 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 439 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 440 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 441 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 442 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 443 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 444 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 445 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 446 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 447 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 448 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 449 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 450 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 451 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 452 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 453 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 454 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 455 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.1s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 456 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 457 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 458 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 459 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 460 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 461 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 462 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 463 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 464 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 465 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 466 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 467 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 468 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 469 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 470 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 471 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 472 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 473 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 474 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 475 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 476 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 477 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 478 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 479 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 480 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 481 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 482 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 483 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 484 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 485 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 486 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 487 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 488 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 489 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 490 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 491 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 492 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 493 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 494 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.5s Forecasting for step 495 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 496 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 497 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 498 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 499 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 500 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 501 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 502 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 503 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 504 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 505 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 506 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 507 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 508 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 509 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 510 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 511 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 512 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 513 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 514 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 515 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 516 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 517 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 518 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 519 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 520 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 521 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 522 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 523 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 524 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 525 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 526 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 527 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 528 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 529 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.1s Forecasting for step 530 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 531 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 532 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 533 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 534 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 535 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 536 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 537 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 538 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 539 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 540 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 541 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 542 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 543 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 544 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 545 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 546 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 547 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 548 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 549 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 550 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 551 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 552 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 553 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 554 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 555 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 556 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 557 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 558 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 559 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 560 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 561 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 562 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 563 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 564 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 565 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 566 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 567 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 568 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 569 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 570 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 571 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 572 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 573 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 574 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 575 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 576 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 577 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 578 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 579 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 580 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 581 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 582 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 583 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 584 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 585 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 586 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 587 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 588 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 589 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 590 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 591 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 592 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 593 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 594 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 595 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 596 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 597 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 598 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 599 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 600 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 601 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 602 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 603 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 604 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 605 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 606 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 607 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 608 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 609 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 610 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 611 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 612 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 613 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 614 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 615 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 616 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 617 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 618 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 619 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 620 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 621 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 622 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 623 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 624 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 625 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 626 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 627 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 628 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 629 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 630 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 631 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 632 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 633 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 634 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 635 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 636 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 637 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 638 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 639 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 640 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.1s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.1s Forecasting for step 641 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 642 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 643 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 644 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 645 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 646 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 647 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 648 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 649 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 650 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 651 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 652 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 653 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 654 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 655 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 656 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 657 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 658 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 659 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 660 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 661 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 662 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 663 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 664 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 665 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 666 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 667 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 668 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 669 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 670 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 671 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 672 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 673 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 674 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 675 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 676 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 677 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 678 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 679 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 680 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 681 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 682 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 683 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 684 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 685 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 686 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 687 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 688 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 689 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 690 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 691 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 692 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 693 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.1s Forecasting for step 694 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 695 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 696 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 697 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 698 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s Forecasting for step 699 Fitting 3 folds for each of 1 candidates, totalling 3 fits [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.4s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s [CV] END model__eta=0.3, model__max_depth=10, model__n_estimators=10, model__seed=42, model__subsample=0.8; total time= 0.0s
forecast_dates, predicted, model = direct_forecast(max_lag=10, training_fraction=.70, forecast_steps=700, model_selection='ridge', dataset=dataset)
ridge_final_prediction = pd.Series(predicted, index=forecast_dates)
Forecasting for step 1 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 2 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 3 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 4 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 5 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 6 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 7 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 8 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 9 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 10 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 11 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 12 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 13 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 14 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 15 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 16 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 17 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 18 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 19 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 20 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 21 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 22 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 23 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 24 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 25 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 26 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 27 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 28 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 29 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 30 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 31 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 32 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 33 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 34 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 35 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 36 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 37 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 38 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 39 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 40 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 41 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 42 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 43 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 44 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 45 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 46 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 47 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 48 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 49 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 50 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 51 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 52 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 53 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 54 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 55 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 56 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 57 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 58 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 59 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 60 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 61 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 62 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 63 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 64 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 65 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 66 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 67 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 68 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 69 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 70 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 71 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 72 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 73 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 74 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 75 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 76 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 77 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 78 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 79 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 80 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 81 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 82 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 83 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 84 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 85 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 86 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 87 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 88 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 89 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 90 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 91 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 92 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 93 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 94 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 95 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 96 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 97 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 98 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 99 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 100 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 101 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 102 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 103 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 104 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 105 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 106 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 107 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 108 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 109 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 110 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 111 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 112 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 113 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 114 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 115 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 116 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 117 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 118 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 119 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 120 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 121 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 122 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 123 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 124 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 125 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 126 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 127 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 128 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 129 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 130 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 131 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 132 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 133 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 134 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 135 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 136 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 137 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 138 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 139 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 140 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 141 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 142 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 143 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 144 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 145 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 146 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 147 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 148 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 149 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 150 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 151 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 152 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 153 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 154 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 155 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 156 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 157 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 158 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 159 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 160 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 161 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 162 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 163 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 164 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 165 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 166 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 167 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 168 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 169 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 170 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 171 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 172 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 173 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 174 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 175 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 176 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 177 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 178 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 179 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 180 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 181 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 182 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 183 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 184 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 185 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 186 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 187 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 188 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 189 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 190 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 191 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 192 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 193 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 194 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 195 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 196 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 197 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 198 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 199 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 200 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 201 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 202 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 203 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 204 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 205 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 206 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 207 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 208 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 209 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 210 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 211 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 212 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 213 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 214 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 215 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 216 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 217 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 218 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 219 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 220 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 221 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 222 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 223 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 224 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 225 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 226 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 227 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 228 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 229 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 230 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 231 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 232 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 233 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 234 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 235 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 236 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 237 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 238 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 239 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 240 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 241 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 242 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 243 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 244 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 245 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 246 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 247 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 248 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 249 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 250 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 251 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 252 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 253 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 254 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 255 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 256 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 257 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 258 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 259 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 260 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 261 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 262 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 263 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 264 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 265 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 266 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 267 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 268 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 269 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 270 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 271 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 272 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 273 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 274 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 275 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 276 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 277 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 278 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 279 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 280 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 281 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 282 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 283 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 284 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 285 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 286 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 287 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 288 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 289 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 290 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 291 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 292 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 293 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 294 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 295 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 296 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 297 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 298 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 299 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 300 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 301 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 302 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 303 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 304 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 305 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 306 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 307 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 308 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 309 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 310 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 311 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 312 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 313 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 314 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 315 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 316 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 317 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 318 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 319 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 320 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 321 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 322 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 323 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 324 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 325 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 326 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 327 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 328 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 329 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 330 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 331 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 332 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 333 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 334 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 335 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 336 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 337 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 338 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 339 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 340 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 341 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 342 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 343 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 344 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 345 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 346 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 347 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 348 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 349 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 350 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 351 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 352 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 353 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 354 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 355 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 356 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 357 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 358 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 359 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 360 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 361 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 362 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 363 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 364 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 365 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 366 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 367 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 368 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 369 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 370 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 371 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 372 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 373 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 374 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 375 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 376 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 377 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 378 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 379 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 380 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 381 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 382 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 383 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 384 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 385 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 386 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 387 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 388 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 389 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 390 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 391 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 392 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 393 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 394 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 395 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 396 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 397 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 398 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 399 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 400 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 401 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 402 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 403 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 404 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 405 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 406 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 407 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 408 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 409 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 410 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 411 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 412 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 413 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 414 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 415 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 416 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 417 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 418 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 419 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 420 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 421 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 422 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 423 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 424 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 425 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 426 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 427 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 428 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 429 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 430 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 431 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 432 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 433 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 434 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 435 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 436 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 437 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 438 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 439 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 440 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 441 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 442 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 443 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 444 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 445 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 446 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 447 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 448 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 449 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 450 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 451 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 452 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 453 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 454 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 455 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 456 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 457 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 458 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 459 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 460 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 461 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 462 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 463 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 464 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 465 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 466 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 467 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 468 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 469 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 470 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 471 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 472 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 473 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 474 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 475 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 476 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 477 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 478 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 479 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 480 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 481 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 482 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 483 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 484 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 485 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 486 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 487 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 488 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 489 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 490 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 491 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 492 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 493 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 494 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 495 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 496 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 497 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 498 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 499 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 500 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 501 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 502 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 503 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 504 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 505 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 506 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 507 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 508 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 509 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 510 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 511 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 512 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 513 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 514 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 515 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 516 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 517 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 518 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 519 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 520 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 521 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 522 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 523 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 524 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 525 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 526 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 527 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 528 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 529 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 530 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 531 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 532 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 533 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 534 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 535 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 536 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 537 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 538 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 539 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 540 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 541 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 542 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 543 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 544 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 545 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 546 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 547 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 548 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 549 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 550 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 551 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 552 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 553 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 554 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 555 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 556 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 557 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 558 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 559 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 560 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 561 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 562 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 563 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 564 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 565 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 566 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 567 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 568 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 569 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 570 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 571 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 572 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 573 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 574 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 575 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 576 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 577 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 578 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 579 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 580 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 581 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 582 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 583 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 584 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 585 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 586 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 587 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 588 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 589 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 590 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 591 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 592 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 593 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 594 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 595 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 596 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 597 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 598 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 599 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 600 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 601 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 602 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 603 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 604 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 605 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 606 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 607 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 608 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 609 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 610 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 611 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 612 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 613 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 614 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 615 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 616 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 617 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 618 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 619 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 620 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 621 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 622 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 623 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 624 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 625 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 626 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 627 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 628 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 629 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 630 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 631 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 632 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 633 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 634 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 635 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 636 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 637 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 638 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 639 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 640 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 641 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 642 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 643 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 644 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 645 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 646 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 647 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 648 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 649 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 650 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 651 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 652 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 653 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 654 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 655 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 656 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 657 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 658 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 659 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 660 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 661 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 662 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 663 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 664 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 665 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 666 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 667 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 668 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 669 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 670 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 671 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 672 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 673 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 674 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 675 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 676 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 677 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 678 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 679 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 680 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 681 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 682 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 683 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 684 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 685 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 686 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 687 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 688 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 689 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 690 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 691 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 692 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 693 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 694 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 695 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 696 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 697 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 698 Fitting 5 folds for each of 5 candidates, totalling 25 fits Forecasting for step 699 Fitting 5 folds for each of 5 candidates, totalling 25 fits
With the models trained, I can plot their results below. The top panel shows a timeseries of observation and forecast predictions from each model. The bottom panel shows scatter plots of observations vs predictions.
fig8 = make_plot('go', y_title='Stage 1 Feed Pressure', x_title='Date')
fig8.add_trace(go.Scatter(x=dataset.index, y=dataset['stage_1_feed_pressure'], name='Observation'))
fig8.add_trace(go.Scatter(x=xgboost_final_prediction.to_frame().index, y=xgboost_final_prediction.to_frame()[0], name='XGBoost Forecast'))
fig8.add_trace(go.Scatter(x=ridge_final_prediction.to_frame().index, y=ridge_final_prediction.to_frame()[0], name='Ridge Forecast'))
fig8.update_layout(height=500)
fig8.update_xaxes(nticks=12)
fig8.update_xaxes(range=['2020-03-01 00:00:00+00:00', '2020-04-20 00:00:00+00:00'])
fig8.update_yaxes(range=[11, 15])
fig9=make_plot('go', x_title='Predicted', y_title='Observed')
fig9.add_trace(go.Scatter(mode='markers',y=dataset.loc[xgboost_final_prediction.to_frame().index, 'stage_1_feed_pressure'], x=xgboost_final_prediction.to_frame()[0], name = 'Obs vs. XGBoost Forecast'))
fig9.add_trace(go.Scatter(mode='markers',y=dataset.loc[ridge_final_prediction.to_frame().index, 'stage_1_feed_pressure'], x=ridge_final_prediction.to_frame()[0], name = 'Obs vs. Ridge Forecast'))
fig9.update_layout(height=500)
fig8.show(renderer="svg", scale=1)
fig9.show(renderer="svg", scale=1)
def calculate_metrics(forecast):
mse = mean_squared_error(y_true=dataset.loc[forecast.to_frame().index, 'stage_1_feed_pressure'],
y_pred=forecast.to_frame()[0])
rmse = mean_squared_error(squared=False, y_true=dataset.loc[forecast.to_frame().index, 'stage_1_feed_pressure'],
y_pred=forecast.to_frame()[0])
mae = mean_absolute_error(y_true=dataset.loc[forecast.to_frame().index, 'stage_1_feed_pressure'],
y_pred=forecast.to_frame()[0])
return mse, rmse, mae
xgb_mse, xgb_rmse, xgb_mae = calculate_metrics(xgboost_final_prediction)
ridge_mse, ridge_rmse, ridge_mae = calculate_metrics(ridge_final_prediction)
xgb_mse, xgb_mae, xgb_rmse
(0.25169201409664543, 0.4580844753729006, 0.501689160832328)
ridge_mse, ridge_rmse, ridge_mae
(0.5410698621571275, 0.7355745116282425, 0.6221929204959428)
XGBoost is better than ridge regressor on all metrics calculated. Both models are better than the ARIMA/X models calculated previously (RMSE = 0.74 and MAE=0.59 for the ARIMAX and ARIMA had RMSE = 0.73 and MAE=0.60). MAE for the ARIMA type models is better since it doesn't penalize large errors as much as RMSE. Given the problem we're trying to solve, RMSE is a more important metric. Small errors in prediction aren't going to be very detrimental, but massive errors could cause the model to miss important events, leading to broken equipment and lost time/money.